Accurate, precise modeling of cell proliferation kinetics from timelapse imaging and automated image analysis of agar yeast culture arrays
 Najaf A Shah†^{1},
 Richard J Laws†^{2},
 Bradley Wardman^{1},
 Lue Ping Zhao^{2} and
 John L HartmanIV^{3}Email author
DOI: 10.1186/1752050913
© Shah et al; licensee BioMed Central Ltd. 2007
Received: 21 November 2006
Accepted: 08 January 2007
Published: 08 January 2007
Abstract
Background
Genomewide mutant strain collections have increased demand for high throughput cellular phenotyping (HTCP). For example, investigators use HTCP to investigate interactions between gene deletion mutations and additional chemical or genetic perturbations by assessing differences in cell proliferation among the collection of 5000 S. cerevisiae gene deletion strains. Such studies have thus far been predominantly qualitative, using agar cell arrays to subjectively score growth differences. Quantitative systems level analysis of gene interactions would be enabled by more precise HTCP methods, such as kinetic analysis of cell proliferation in liquid culture by optical density. However, requirements for processing liquid cultures make them relatively cumbersome and low throughput compared to agar. To improve HTCP performance and advance capabilities for quantifying interactions, YeastXtract software was developed for automated analysis of cell array images.
Results
YeastXtract software was developed for kinetic growth curve analysis of spotted agar cultures. The accuracy and precision for image analysis of agar culture arrays was comparable to OD measurements of liquid cultures. Using YeastXtract, image intensity vs. biomass of spot cultures was linearly correlated over two orders of magnitude. Thus cell proliferation could be measured over about seven generations, including four to five generations of relatively constant exponential phase growth. Spot area normalization reduced the variation in measurements of total growth efficiency. A growth model, based on the logistic function, increased precision and accuracy of maximum specific rate measurements, compared to empirical methods. The logistic function model was also more robust against data sparseness, meaning that less data was required to obtain accurate, precise, quantitative growth phenotypes.
Conclusion
Microbial cultures spotted onto agar media are widely used for genotypephenotype analysis, however quantitative HTCP methods capable of measuring kinetic growth rates have not been available previously. YeastXtract provides objective, automated, quantitative, image analysis of agar cell culture arrays. Fitting the resulting data to a logistic equationbased growth model yields robust, accurate growth rate information. These methods allow the incorporation of imaging and automated image analysis of cell arrays, grown on solid agar media, into HTCPdriven experimental approaches, such as global, quantitative analysis of gene interaction networks.
Background
Most genetic research is aimed ultimately at understanding how phenotypes are produced. This is complicated by the fact that genes interact with the environment and other genes in producing phenotypes, such that the phenotypic effect of mutating any single gene depends on the allele status at secondary loci as well as environmental variables [1]. Largescale phenotypic analysis of combinations of genetic and environmental variations (perturbations) has proven useful for understanding the organization of gene networks [2–4]. However, analysis of gene interactions is not tractable in humans due to their outbred nature and phenotypic complexity [5], thus genetically tractable model systems can provide new inroads for understanding genotypephenotype complexity of human disease pathways [6, 7]. In this regard, the collection of 5000 yeast gene deletion strains provides a unique resource for systematic analysis of gene interactions by comparing cell proliferation phenotypes (CPPs) of the WT strain and each deletion mutant under various perturbation conditions [2–4, 8, 9].
Most largescale phenotypic analyses of the yeast gene deletion strains have been nonor semiquantitative, based on endpoint analysis of cell proliferation [10]. On a smaller scale, quantitative analysis of gene interactions has proven advantageous by virtue of being more objective, sensitive, and discriminating between strength of interactions, which can aid identification of distinct pathways represented within large sets of interacting genes [2, 11–14]. Precise quantitative phenotyping together with kinetic analysis of cell proliferation can reveal differential genetic regulation of distinct physiological phases of growth [15, 16]. Ideally, HTCP would have sufficient throughput and quantitative accuracy for investigating genotypephenotype complexity with respect to many dimensions including time, different kinetic features of cell proliferation, genegene and geneenvironment perturbation combinations, and gradients of perturbation intensity. These dimensions may be critical to parse gene networks functionally.
Turbidity readings of liquid cultures are the current standard for kinetic analysis of microbial cell proliferation [12, 16]. However, throughput is greatly reduced, relative to endpoint analysis of agar spotted arrays, or the use of DNA microarray hybridization methods [4, 8, 17–19]. Throughput is lower for kinetic vs. endpoint analysis because ~30 time points of data are taken for each culture. Furthermore, liquid arrays are more difficult to analyze than solid arrays due to shaking requirements for resuspending cells prior to each reading, and increased time for operation of a microplate reader vs. visual inspection. Precision of kinetic turbidity readings is limited by spilling, cross contamination, and evaporation, which hinders miniaturization and automation of liquid culturebased HTCP. Phenotypic Array Analysis (PAA), an alternative quantitative HTCP approach based on timelapse imaging and image analysis of agar spotted cell arrays, improves throughput to ~25,000–100,000 measurements per hour [2], taking advantage of the easy handling and potential for rapid imaging of agar cell arrays. This work describes YeastXtract, an image analysis software application that improves PAA, so that early phase kinetic growth rates can be measured, analogous to OD readings of liquid cultures. Validation experiments are presented for YeastXtract. Additionally, the logistic growth equation was used for kinetic modeling of cell proliferation data and shown to offer advantages over empirical growth models for quantifying cell proliferation phenotypes from time series images. Together, these methods are intended to improve HTCP capacity for global, quantitative analysis of gene interactions using large microbial mutant collections.
Results and Discussion
YeastXtract image analysis software
YeastXtract is a software application that analyzes time series images of yeast cell arrays, for the purpose of kinetic growth curve analysis, and can be used on operating systems with the Java platform installed. From the YeastXtract user interface, a sequence of images is selected using a 'Browse' function, and automated analysis is initiated by selecting the "Start Analysis" button. After analysis is complete, the enumerated intensities and areas of culture spots are displayed. Timelapse images of individual spot cultures, along with plotted growth curves can be accessed via the 'Spot Level Information' tab. Accuracy of spot detection can be checked using the 'Spot Detection' function which depicts the ellipses used to quantify biomass of each culture on the cell array image. A user manual with screenshots depicting how these functions are accessed from the user interface is provided as [see Additional file 1]. The software executables, source code, and sample images are available for download [20] and use under the Creative Commons AttributionNonCommercialShareAlike 2.5 license [21]. The software has a modular design to facilitate modification and further development. An overview of the analysis algorithm is provided below, with a detailed description in Methods.
YeastXtract provides accuracy and precision for image analysis of agar culture arrays comparable to optical density readings of liquid cultures
The original aim of this study was to increase the sensitivity for detecting spotted cell cultures to reach the range and accuracy of microplate readers for kinetic growth analysis. Our previous image analysis programs did not have the sensitivity to measure specific growth rates when they were in their maximal steady state [2]. Spot detection and local background subtraction were implemented to increase accuracy and precision of intensity measures. Background subtraction is also useful for modeling growth phenomena, since the background is nonbiological and can contribute substantially (~25%) to the final spot intensity.
Normalization of spot intensity by spot area reduces variation in FPI and AUGC
Comparison of cell proliferation phenotypes calculated with three different models. Median and percent standard deviation values are shown for four different CPPs calculated by the three growth models tested. Time series spot intensity data from 96 replicate cultures (one cell array) were used [see Additional file 4]. Percent standard deviation is calculated as the standard deviation divided by the median × 100.
MSR  FPI  TMR  AUGC  

Model  Median  %Std. Dev.  Median  %Std. Dev.  Median  %Std. Dev.  Median  %Std. Dev. 
Raw  .40  23.8  7.96e+4  10.1  29.8  4.4  3.13e+6  9.6 
Spline  .37  9.1  7.96e+4  10.1  27.3  2.0  3.25e+6  9.5 
Logistic  .35  4.6  6.7e+4  10.2  26.2  1.2  2.99e+6  9.9 
Effect of spot area normalization on cell proliferation phenotypes. Areanormalized spot intensities were used in place of total intensities to compare the three growth models, as was done in Table 1. Spot area normalization reduced the percent variation for FPI and AUGC, while not affecting MSR or TMR.
MSR  FPI  TMR  AUGC  

Model  Median  %Std. Dev.  Median  %Std. Dev.  Median  %Std. Dev.  Median  %Std. Dev. 
Raw  .40  23.8  119.1  4.6  29.8  4.4  4724  5.6 
Spline  .37  9.1  119.1  4.6  27.3  2.0  4886  5.2 
Logistic  .35  4.1  100.4  6.9  26.2  1.0  4479  6.9 
A logistic function model is used to quantify cell proliferation phenotypes, such as maximum specific rate and total growth efficiency, from time series data

Total Growth Efficiency, which is measured by the Final Population Intensity (FPI) of a spot culture, is also referred to as the carrying capacity in the logistic equation.

Specific Growth Rate is the growth rate divided by the population size.

Maximum Specific Growth Rate (MSR) is the maximum value of the specific rate over time, and is inversely proportional to the minimum doubling time of a culture.

Doubling Time is the time required for the population size to double. Minimum doubling time is equal to log_{e} 2/MSR.

Area Under Growth Curve (AUGC) is the integral of spot intensity curve over the interval between the first and final time point.

Time of Maximum Rate (TMR) corresponds to the time when the growth rate reaches its peak value; by the logistic model, TMR marks the time when half carrying capacity is reached.

Lag Time is a property of the culture, whereby there is a delay after cells are introduced into a new medium before MSR is achieved.
To evaluate the performance of different growth models, we considered reduction in the variation of CPP values from many replicate cultures as an increase in the precision of a model (Tables 1 and 2). The following form of the logistic equation was used to fit growth data:
$G(t)=\frac{K}{1+{e}^{r(tl)}},G(0)<K$
where K ("carrying capacity") is approximated by the FPI; r is the MSR, and l is the TMR. We compared CPPs derived from the logistic equation model, the raw data, and data fit to a spline model (see Methods for more details about the models).
The logistic function growth model increases precision of MSR and TMR measurements
Median MSR values were comparable, regardless of the model used for calculation (Table 1), with minimum doubling times ranging between 1.75 (MSR = .40) and 1.98 (MSR = .35) hours. However, the variation in MSR values was reduced by 63% (24% vs. 9%) if calculated using splinefit data instead of raw data (Table 1, see Additional file 4). MSR variation was reduced another 44% (9% vs. 5%) using the logistic model (Table 1). Variation in the calculation of TMR was similarly improved by the spline and logistic equationfitted data. The likely explanation for the reduced variation in the splinefit vs. the raw data is that growth is a continuous function, and thus fitting of the data increases precision by reducing the time interval for rate calculations. Increases in measurement precision for MSR and TMR with the logistic equation may stem from it being specifically designed for modeling growth phenomena [22, 23].
AUGC measurements were not greatly impacted by the model used. Likewise, FPI, which is a dominant factor in AUGC calculation, is relatively unaffected by model selection (Table 1). There was a trend toward lower FPI and AUGC with the logistic model (Fig. 5), which was investigated by examining the nature of FPI in more detail, as described below.
An 'initial carrying capacity' is modeled by the logistic equation
In Fig. 5, these phenomena are depicted by a time series of spot intensities for a typical edge culture, where an inflection in the growth curve occurs after initial carrying capacity is reached (between 40 and 45 hrs). Fitting the data to a spline, the late increase in spot intensity is followed closely (Fig. 5b). However fitting the same data to the logistic equation, this inflection in the spot intensity curve is missed (Fig. 5c). In summary, the area of agar initially covered by cells at the time of array printing grows to confluence, reaching an "initial carrying capacity"; and further increases in spot intensities are correlated with actual increase in the size of the spot (Fig. 6), which is not well modeled by the logistic equation.
Data are filtered after the time initial carrying capacity is reached to improve modeling
To better understand the nature of the initial carrying capacity, the difference in spot area after 39 and 70 hours of growth was examined, confirming that edge cultures increase in size more than internal cultures (Fig. 6a). We next examined the growth rate with respect to time and spot area, finding that increases in spot area correspond with an inflection in the growth rate curve (Fig. 6b). Thus, once spot cultures have reached their initial carrying capacity (the maximum population yield over the original area for the spotted culture), further increases are associated with increases in the spot area, occurring preferentially at the edges of a cell array.
To improve growth curve modeling with the logistic equation, we designed a filtering algorithm to reduce the effects that increases is spot area might have after initial carrying capacity is reached, since individual cultures in an experiment might have varying growth rates due to gene deletions and/or other perturbations. Since the logistic equation has the property that the maximum growth rate occurs when population is at half of carrying capacity, we used a spline to estimate the TMR and then filtered out time points having greater than 2.2 times the spot intensity at TMR. The filtering algorithm improves fitting of data to the logistic model by reducing the tendency for artificial increases in FPI for cultures on the edge of an array (Fig. 6b).
Physiological lag time can be measured directly by Phenotypic Array Analysis
The logistic equationbased growth model is robust against data sparseness
The robustness of the CPPs obtained from the logistic model likely results from the appropriateness of assumptions inherent to its equation for cell proliferation phenomena; the main assumption being that the rate of increase in biomass at any time is proportional to the biomass and the availability of resources [22, 23]. A major strength of this form of the logistic equation is that its two major parameters, K and r, correlate well with FPI and MSR under standard conditions for growing spotted cultures on agar media.
Conclusion
Global, systematic analysis of gene interaction networks is a recent experimental paradigm for systems biology. Since genetic interactions are often scored on the basis of cell proliferation measurements, HTCP is an enabling technology for this field of research. YeastXtract and the growth modeling algorithms presented here, help advance HTCP throughput and accuracy to enable phenotypic measurements in different dimensions such as varying intensities of perturbations, and different physiological aspects of growth responses (e.g., lag, maximum growth rates, and total growth efficiency). These advances will allow interactions to be investigated not only from the perspective of different combinations of gene and environmental/chemical perturbations, but also different aspects of the growth phenotype itself, each of which may be sculpted by different natural selective pressure for gene activities.
In a previous publication, we described Phenotypic Array Analysis, an HTCP method based on rapid imaging of ~25,000 spotted cultures per hour [2]. YeastXtract now enables automated PAA, without need for manual preprocessing of images. It provides single pixel resolution, improving PAA sensitivity and accuracy. While the methods were developed using yeast, and intended for application to the set of 5000 yeast gene deletion strains, they should also be applicable to other cell types that can be grown in similar fashion as agar cell arrays. Imaging and automated image analysis of cell arrays can now be incorporated into HTCPdriven experimental approaches, such as for quantitative investigations of gene interaction networks [1, 2]. Looking forward, insight from global, quantitative analysis of gene interaction networks in single cell organisms, should be extensible for hypothesisdriven investigations of cellular pathways that buffer genetic and environmental perturbations in an orthologous fashion in multicellular organisms [24, 25].
Methods
Strains and media
All experiments were performed with BY4741 strain (MATa ura3 leu2 his3 met15). Pregrowth was in YPD liquid media, dilutions were in water, and growth measurements were on synthetic complete media [26].
Cell array printing and imaging
Cultures were grown as a single overnight culture and diluted in water prior to spotting 4 μL drops onto agar plates containing synthetic complete media, as previously described [2]. The plates were incubated at 30°C, and periodically removed and imaged on an Epson Expression 10,000 XL scanner operating in transmitted light mode. Images were collected at 140 dpi and 8bit grayscale. Time stamps on the image files were used for generating growth curves after image analysis.
YeastXtract (image analysis)
The algorithm was devised by building upon experience gained from development of a previous software program, SignalViewer [27, 28], and consists of three main processes:
1. Plate extraction and alignment
A set S, consisting of a time series of images of up to 10 cell arrays, was processed as a group. Thus, for a single scan configuration imaged k times, the image analysis algorithm requires the following input:

S, a set of k TIFF images,

n, the number of plates on the scan,

p, the pitch, or the expected distance (in pixels) between the centers of two adjacent spots,

d, the approximate expected length in pixels of a typical spot's diameter,

L, a set of predefined horizontal and vertical coordinates that denote the location of each cell array ('plate') on a 'scan', containing up to 10 plates

r, number of culture rows on each array, and

c, number of culture columns on each array.
The predefined pixel coordinates in L for the position of each plate on a scan are used to extract each plate at all k time points. Because plates are manually placed on the scanning surface, a particular plate can be in slightly different locations on scans imaged at two different times. To minimize the effect of translocation on extraction of spot intensities, all k images are aligned using a least squares algorithm. Beginning with the nexttolast timepoint, each image is aligned with the image immediately after it in time. Using the later image as a reference, the image is shifted by α to +α pixels in the horizontal direction and β to +β pixels in the vertical direction and the squareddifference in the pixel intensities of the two images is calculated for each combination of α and β. The image is shifted by the combination of α and β that results in the lowest difference between the two images. Using α = β = 4, the best alignment among 81 possible is selected.
2. Spot detection
During the spot "detection" phase, the final image from the time series is used to identify the spot locations. First, the rectangular regions containing each spot are determined by considering columns of pixels one at a time. The 75^{th} percentile value of the pixel intensities in each column is calculated and the resultant value is stored in an array. This procedure is repeated for all pixel rows of the plate image and the intersection of the peak values of rows and columns having the highest 75^{th} percentile values are used to identify the approximate center of each spot, as depicted in Figure 1c. However, before detecting the peaks, the values in the row and column percentile arrays are processed using the LOESS smoothing algorithm with a smoothing parameter value of 0.03; we have found that this additional processing makes the algorithm more robust by filtering away noise on the image that may cause the algorithm to erroneously detect spot culture centers. The intersections of the row and column peaks form a grid representing the approximate locations of the spot centers. Given these centers and p, the approximate pitch, the rectangular region encapsulating a culture spot (approximately p^{2} in size) can be extracted from the plate image. This procedure is repeated for all culture spots on the plate.
Next, the precise position of each spot within its region is determined by again identifying peaks in the row and column percentile arrays. All k images of each culture spot are collected and then aligned using the least squares method described for aligning whole plates. The image of the culture spot from the final time point is analyzed to determine the coordinates of an elliptical region that circumscribes the spot by summing the pixel intensities in each column and row. LOESS smoothing algorithm was used to process row and column sums with a smoothing parameter of 0.25. The locations of the peaks and the locations where the row and column sums rise above a threshold are used to compute the horizontal and vertical coordinates of the center and the two diameters of the ellipse, respectively (Fig. 1d):

$\frac{{x}^{2}}{{a}^{2}}+\frac{{y}^{2}}{{b}^{2}}=1$ (General equation of an ellipse)

E_{ cf }first pixel column where column sum is greater than threshold,

E_{ cl }last pixel column where column sum is greater than threshold,

E_{ rf }first pixel row where row sum is greater than threshold,

E_{ rl }last pixel row where row sum is greater than threshold,

a = E_{ cl } E_{ cf }

b = E_{ rl } E_{ rf }

h = pixel column where column sum is highest

k = pixel row where row sum is highest
3. Signal extraction
The background of the culture spot is determined by computing the mode of the intensities of the pixels outside the ellipse, but within the area containing the ellipse, and then taking a local average around that mode. This background intensity is then subtracted from all images of this culture spot. For each image belonging to a particular culture spot, pixel intensities inside the ellipse are summed. The area of the ellipse circumscribing each culture spot is calculated by counting the number of pixels inside the ellipse.
Spot culture biomass measurements
For figure 3, 96 spot cultures were cut out immediately after imaging and resuspended in 2 ml of icecold water by vortexing the agar plug. An appropriate fraction of the cell suspension was then taken for particle analysis (~5 × 10^{6} total cells), and transferred to 10 mL of ice cold saline (Isoton, Beckman). A Z2 Coulter Counter (Beckman) with 70 um aperture (particle size 10 – 350 uL) was used for particle analysis.
Kinetic growth modeling
Custom Matlab programs (available at [20]) were used for modeling growth curves from kinetic spot intensity data. Three different methods were used to calculate Cell Proliferation Phenotypes for 96 cultures from spot intensities. CPPs were calculated directly from the raw spot intensities in the first method and from logistic and splinefitted models in the second and third methods, respectively. For the first method, the final recorded intensity was used as the FPI, Riemann sum was used to calculate the AUGC, and the MSR was determined by calculating the percent change in spot intensity with respect to time between consecutive points and recording the maximum among those values, as follows:

G_{ raw }(t) = Spot intensity at time t.

FPI_{ raw }= Spot intensity at final timepoint; i.e. G_{ raw }(t_{ final }).

$AUG{C}_{raw}={\displaystyle \sum _{i=1}^{n}G({t}_{i})\ast \Delta {t}_{i}}$ where n = number of timepoints  1 (Riemann sum).

$Rat{e}_{raw}({t}_{i})=\frac{G({t}_{i})G({t}_{i1})}{{t}_{i}{t}_{i1}}$

$SpecificRat{e}_{raw}({\text{t}}_{\text{i}})=\frac{Rat{e}_{raw}({t}_{i})}{G({t}_{i})}$

MSR_{ raw }= maximum value of Specific Rate_{ raw }over [0, t_{ final }].

TMR_{ raw }= t_{i} where Rate_{ raw }(t_{ i }) is maximal over [0, t_{ final }].
For the second method, the raw data were first fit to a cubic smoothing spline and the resulting function was transformed to a Bspline (a generalization of the Bézier curve). The spline function was integrated to calculate the AUGC, and it was evaluated at the last timepoint to obtain FPI. The specific rate was calculated as the derivative with respect to time, divided by the function (i.e., population growth rate divided by population size), and the MSR was determined from these values. Spot intensities less than 1000 (a conservative threshold for image sensitivity) were not considered in MSR calculation for the spline and raw models (see figure 5).
For growth curve modeling with the logistic equation, the Curve Fitting Toolbox in Matlab was used. Time series data were first filtered to eliminate values that exceeded the initial carrying capacity by more than 10% (see Figs. 5 and 6). An estimate of the initial carrying capacity was determined by first using a smoothing spline to determine the TMR. The spot intensity at TMR was multiplied by 2.2 to estimate the carrying capacity (according to the logistic equation, the population size is at half its carrying capacity at TMR). The TMR spot intensity was scaled by 2.2, instead of 2, to prevent excessive filtering. The following form of the logistic equation was next used to fit the filtered data:
$G(t)=\frac{K}{1+{e}^{r(tl)}},G(0)<K$
The logistic model returns values for the parameters, K, r, and l. K is the initial carrying capacity approximating the FPI; r is equivalent to the MSR, and l is equivalent to TMR.
Notes
Abbreviations
 AUGC :

Area Under Growth Curve.
 CPP :

Cell Proliferation Phenotype
 FPI :

Final Population Intensity
 HTCP :

High Throughput Cellular Phenotyping
 MSR :

Maximum Specific growth Rate.
 PAA :

Phenotypic Array Analysis
 TMR :

Time when Maximum growth Rate is observed
Declarations
Acknowledgements
The authors are grateful to Lee Hartwell, for support with development of PAA and YeastXtract; and to Jacob Cheng, Whipple Neely, Xiaohong Li, and Wei Li for programming assistance. The work was supported by grants awarded to JLH from NIH (K08CA90637) and HHMI (PhysicianScientist Postdoctoral Fellowship and PhysicianScientist Early Career Award), and Lee Hartwell (NIH GM17709).
Authors’ Affiliations
References
 Hartman IV JL, Garvik B, Hartwell L: Principles for the buffering of genetic variation. Science. 2001, 291 (5506): 10011004.View ArticleGoogle Scholar
 Hartman IV JL, Tippery NP: Systematic quantification of gene interactions by phenotypic array analysis. Genome Biol. 2004, 5 (7): R49, 10.1186/gb200457r49View ArticleGoogle Scholar
 Parsons AB, Brost RL, Ding H, Li Z, Zhang C, Sheikh B, Brown GW, Kane PM, Hughes TR, Boone C: Integration of chemicalgenetic and genetic interaction data links bioactive compounds to cellular target pathways. Nat Biotechnol. 2003Google Scholar
 Tong AH, Lesage G, Bader GD, Ding H, Xu H, Xin X, Young J, Berriz GF, Brost RL, Chang M, Chen Y, Cheng X, Chua G, Friesen H, Goldberg DS, Haynes J, Humphries C, He G, Hussein S, Ke L, Krogan N, Li Z, Levinson JN, Lu H, Menard P, Munyana C, Parsons AB, Ryan O, Tonikian R, Roberts T, Sdicu AM, Shapiro J, Sheikh B, Suter B, Wong SL, Zhang LV, Zhu H, Burd CG, Munro S, Sander C, Rine J, Greenblatt J, Peter M, Bretscher A, Bell G, Roth FP, Brown GW, Andrews B, Bussey H, Boone C: Global mapping of the yeast genetic interaction network. Science. 2004, 303 (5659): 808813. 10.1126/science.1091317PubMedView ArticleGoogle Scholar
 Barton NH, Keightley PD: Understanding quantitative genetic variation. Nat Rev Genet. 2002, 3 (1): 1121. 10.1038/nrg700PubMedView ArticleGoogle Scholar
 Badano JL, Katsanis N: Beyond Mendel: an evolving view of human genetic disease transmission. Nat Rev Genet. 2002, 3 (10): 779789. 10.1038/nrg910PubMedView ArticleGoogle Scholar
 Moore JH: The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered. 2003, 56 (13): 7382. 10.1159/000073735PubMedView ArticleGoogle Scholar
 Giaever G, Flaherty P, Kumm J, Proctor M, Nislow C, Jaramillo DF, Chu AM, Jordan MI, Arkin AP, Davis RW: Chemogenomic profiling: identifying the functional interactions of small molecules in yeast. Proc Natl Acad Sci U S A. 2004, 101 (3): 793798. 10.1073/pnas.0307490100PubMed CentralPubMedView ArticleGoogle Scholar
 Hartman IV JL: Genetic and Molecular Buffering of Phenotypes. Nutritional Genomics: Discovering the Path to Personalized Nutrition. Edited by: Rodriguez R, Kaput J. 2006, 1: 496Hoboken, NJ , John Wiley & Sons, 1Google Scholar
 Scherens B, Goffeau A: The uses of genomewide yeast mutant collections. Genome Biol. 2004, 5 (7): 229, 10.1186/gb200457229PubMed CentralPubMedView ArticleGoogle Scholar
 Drees BL, Thorsson V, Carter GW, Rives AW, Raymond MZ, AvilaCampillo I, Shannon P, Galitski T: Derivation of genetic interaction networks from quantitative phenotype data. Genome Biol. 2005, 6 (4): R38 10.1186/gb200564r38PubMed CentralPubMedView ArticleGoogle Scholar
 Lee W, St Onge RP, Proctor M, Flaherty P, Jordan MI, Arkin AP, Davis RW, Nislow C, Giaever G: GenomeWide Requirements for Resistance to Functionally Distinct DNADamaging Agents. PLoS Genet. 2005, 1 (2): e24 10.1371/journal.pgen.0010024PubMed CentralPubMedView ArticleGoogle Scholar
 Collins SR, Schuldiner M, Krogan NJ, Weissman JS: A strategy for extracting and analyzing largescale quantitative epistatic interaction data. Genome Biol. 2006, 7 (7): R63 10.1186/gb200677r63PubMed CentralPubMedView ArticleGoogle Scholar
 Keith CT, Borisy AA, Stockwell BR: Multicomponent therapeutics for networked systems. Nat Rev Drug Discov. 2005, 4 (1): 7178. 10.1038/nrd1609PubMedView ArticleGoogle Scholar
 FernandezRicaud L, Warringer J, Ericson E, Pylvanainen I, Kemp GJ, Nerman O, Blomberg A: PROPHECYa database for highresolution phenomics. Nucleic Acids Res. 2005, 33 (Database Issue): D369D373. 10.1093/nar/gki126PubMed CentralPubMedView ArticleGoogle Scholar
 Warringer J, Ericson E, Fernandez L, Nerman O, Blomberg A: Highresolution yeast phenomics resolves different physiological features in the saline response. Proc Natl Acad Sci U S A. 2003, 100 (26): 1572415729. 10.1073/pnas.2435976100PubMed CentralPubMedView ArticleGoogle Scholar
 Davierwala AP, Haynes J, Li Z, Brost RL, Robinson MD, Yu L, Mnaimneh S, Ding H, Zhu H, Chen Y, Cheng X, Brown GW, Boone C, Andrews BJ, Hughes TR: The synthetic genetic interaction spectrum of essential genes. Nat Genet. 2005, 37 (10): 11471152., 10.1038/ng1640PubMedView ArticleGoogle Scholar
 Parsons AB, Geyer R, Hughes TR, Boone C: Yeast genomics and proteomics in drug discovery and target validation. Prog Cell Cycle Res. 2003, 5: 159166.PubMedGoogle Scholar
 Giaever G, Chu AM, Ni L, Connelly C, Riles L, Veronneau S, Dow S, LucauDanila A, Anderson K, Andre B, Arkin AP, Astromoff A, ElBakkoury M, Bangham R, Benito R, Brachat S, Campanaro S, Curtiss M, Davis K, Deutschbauer A, Entian KD, Flaherty P, Foury F, Garfinkel DJ, Gerstein M, Gotte D, Guldener U, Hegemann JH, Hempel S, Herman Z, Jaramillo DF, Kelly DE, Kelly SL, Kotter P, LaBonte D, Lamb DC, Lan N, Liang H, Liao H, Liu L, Luo C, Lussier M, Mao R, Menard P, Ooi SL, Revuelta JL, Roberts CJ, Rose M, RossMacdonald P, Scherens B, Schimmack G, Shafer B, Shoemaker DD, SookhaiMahadeo S, Storms RK, Strathern JN, Valle G, Voet M, Volckaert G, Wang CY, Ward TR, Wilhelmy J, Winzeler EA, Yang Y, Yen G, Youngman E, Yu K, Bussey H, Boeke JD, Snyder M, Philippsen P, Davis RW, Johnston M: Functional profiling of the Saccharomyces cerevisiae genome. Nature. 2002, 418 (6896): 387391. 10.1038/nature00935PubMedView ArticleGoogle Scholar
 Hartman Lab Open Wetware. http://openwetware.org/wiki/Hartman_Lab
 Creative Commons License 2.5. http://creativecommons.org/licenses/byncsa/2.5/
 Tsoularis A, Wallace J: Analysis of logistic growth models. Math Biosci. 2002, 179 (1): 2155., 10.1016/S00255564(02)000962PubMedView ArticleGoogle Scholar
 Alocilja EC: Principles of Biosystems Engineering. 2002, Erudition BooksGoogle Scholar
 Tischler J, Lehner B, Chen N, Fraser AG: Combinatorial RNA interference in C. elegans reveals that redundancy between gene duplicates can be maintained for more than 80 million years of evolution. Genome Biol. 2006, 7 (8): R69, 10.1186/gb200678r69PubMed CentralPubMedView ArticleGoogle Scholar
 Lehner B, Crombie C, Tischler J, Fortunato A, Fraser AG: Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nat Genet. 2006, 38 (8): 896903. 10.1038/ng1844PubMedView ArticleGoogle Scholar
 Burke D, Dawson D, Stearns T: Methods in Yeast Genetics. 2000, CSHL PressGoogle Scholar
 Laws RJ, Bergemann TL, Quiaoit F, Zhao LP: SignalViewer: analyzing microarray images. Bioinformatics. 2003, 19 (13): 17161717. 10.1093/bioinformatics/btg208PubMedView ArticleGoogle Scholar
 Bergemann TL, Laws RJ, Quiaoit F, Zhao LP: A statistically driven approach for image segmentation and signal extraction in cDNA microarrays. J Comput Biol. 2004, 11 (4): 695713. 10.1089/cmb.2004.11.695PubMedView ArticleGoogle Scholar
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